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util.py
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util.py
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import os
from dotenv import load_dotenv
import math
import time
import random
import numpy as np
from time import sleep
from datetime import datetime, timezone, timedelta
import pymongo
import statistics
import plot
monthMap = {
"01": "31",
"02": "28",
"03": "31",
"04": "30",
"05": "31",
"06": "30",
"07": "31",
"08": "31",
"09": "30",
"10": "31",
"11": "30",
"12": "31",
}
def dateNow():
dt1 = datetime.utcnow().replace(tzinfo=timezone.utc)
dt2 = dt1.astimezone(timezone(timedelta(hours=8)))
return dt2.strftime("%Y-%m-%d")
def timeNow():
dt1 = datetime.utcnow().replace(tzinfo=timezone.utc)
dt2 = dt1.astimezone(timezone(timedelta(hours=8)))
return dt2.strftime("%Y-%m-%d %H-%M-%S")
# log txt file
def log(user, function):
path = './log.txt'
f = open(path, 'a', encoding='UTF-8')
print(f'time: {timeNow()}, user: {user}, {function}', file=f)
f.close()
async def copySimulate(traderName, margin = 10, lossPerPosition = 100, startDate = "2023-02-01"):
load_dotenv()
client = pymongo.MongoClient(os.getenv('MONGODB'))
drawdownDB = client.TraderDrawdown.traderDrawdown
# lastday = monthMap[month.split('-')[1]]
traderData = drawdownDB.find({"traderName": traderName})
traderData = (list(traderData))
if len(traderData) == 0 :
hasDrawdownData = False
# print('Cannot find trader data in DB.')
return False
else:
traderData = traderData[0]
positionCount = 0
suggestCapital = 0.00
today = dateNow()
startTimestamp = int(time.mktime(time.strptime(f"{ startDate } 00:00:00", "%Y-%m-%d %H:%M:%S")))
endTimestamp = int(time.mktime(time.strptime(f"{ today } 23:59:59", "%Y-%m-%d %H:%M:%S")))
pnlInDays = np.zeros(int((endTimestamp-startTimestamp)/86400), dtype=float)
traderShareRatio = 0.1
totalTraderShare = 0.00
copyProfit = 0.00
hasDrawdownData = True
history = traderData["history"]
traderId = traderData["traderId"]
countSL = 0
for each in history:
openTimestamp = int(time.mktime(time.strptime(each["openDate"], "%Y-%m-%d %H:%M:%S")))
closeTimestamp = int(time.mktime(time.strptime(each["closeDate"], "%Y-%m-%d %H:%M:%S")))
leverage = each["leverage"]
drawdown = each["drawdown"]
if startTimestamp <= openTimestamp and closeTimestamp <= endTimestamp:
positionCount += 1
day = int((closeTimestamp-startTimestamp)/86400)
# 手續費6%, 開倉平倉各付一次
fee = 0.0006 * margin * leverage * 2
if drawdown >= lossPerPosition :
pnl = (-1 * lossPerPosition / 100) * margin
countSL += 1
else:
pnl = (margin * float(each["revenue"])/100) - fee
if pnl > 0:
pnlInDays[day] += pnl * (1 - traderShareRatio)
totalTraderShare += pnl * traderShareRatio
copyProfit += pnl * (1 - traderShareRatio)
else:
pnlInDays[day] += pnl
copyProfit += pnl
# PLOT -------------------------------------
x = []
y = []
y.append(pnlInDays[0])
for i in range(len(pnlInDays)):
x.append(i + 1)
if i == 0:
pass
else:
y.append(pnlInDays[i] + y[i - 1])
filename = plot.plotLine(x, y, startDate, today)
# ------------------------------------------
client.close()
return {
"traderName": traderName, # 交易員名稱
"positionCount": positionCount, # 總計開倉數
"pnlInDays": pnlInDays, # 每日收益
"totalTraderShare": totalTraderShare, # 帶單員分潤收益
"copyProfit": round(copyProfit, 2), # 跟單收益
"suggestCapital": suggestCapital, # 建議本金
"countSL": countSL, # 共計止損？次
"filename": filename, # 圖表檔案名稱
}
def potentialExtremeMaxDrawdown(drawdownData, turns):
drawdownList = []
roundList = []
potentialMaxDrawdown = 0
for r in range(len(turns)):
roundList.append(turns[r][0])
for row in drawdownData:
drawdownList.append(round(float(row["drawdown"]) / 100, 2))
while (len(drawdownList) < sum(roundList)):
drawdownList.append(
drawdownList[random.randint(0, len(drawdownList) - 1)])
maxPosition = max(roundList)
drawdownList.sort(reverse=True)
for i in range(maxPosition):
potentialMaxDrawdown = potentialMaxDrawdown + drawdownList[i]
return potentialMaxDrawdown
async def analyzeTraderMDD(traderName, initialCapital = 10000, maxLossPercent = 20, startDate = "2023-02-01"):
# Initialize
load_dotenv()
client = pymongo.MongoClient(os.getenv('MONGODB'))
drawdownDB = client.TraderDrawdown.traderDrawdown
today = dateNow()
hasDrawdownData = False
# Query in drawdown DB
traderData = drawdownDB.find({"traderName": traderName})
traderData = (list(traderData))
# print(traderData)
if len(traderData) == 0 :
hasDrawdownData = False
# print('Cannot find trader data in DB.')
return False
else:
traderData = traderData[0]
hasDrawdownData = True
startTimestamp = int(time.mktime(time.strptime(f"{ startDate } 00:00:00", "%Y-%m-%d %H:%M:%S")))
endTimestamp = int(time.mktime(time.strptime(f"{ today } 23:59:59", "%Y-%m-%d %H:%M:%S")))
history = traderData["history"]
traderId = traderData["traderId"]
drawdownData = []
for each in history:
openTimestamp = int(time.mktime(time.strptime(each["openDate"], "%Y-%m-%d %H:%M:%S")))
closeTimestamp = int(time.mktime(time.strptime(each["closeDate"], "%Y-%m-%d %H:%M:%S")))
if startTimestamp <= openTimestamp and closeTimestamp <= endTimestamp:
drawdownData.append(each)
client.close()
# Round
turns = []
operating = []
RevenueVSDrawback = []
if hasDrawdownData == True:
for row in drawdownData:
remove = [] # 存放要刪除的筆數
# ---more data--
if row["drawdown"] <= 0: # 無回撤通常伴隨小收益，負回撤也歸類到1
RevenueVSDrawback.append(1)
else:
RevenueVSDrawback.append(row["revenue"] / row["drawdown"])
# --------------
for trade in operating:
# 新 trade 與在場上的 trade 時間不重疊
if time.mktime(time.strptime(row["closeDate"], "%Y-%m-%d %H:%M:%S")) < time.mktime(time.strptime(trade["openDate"], "%Y-%m-%d %H:%M:%S")):
remove.append(trade)
if len(remove) != 0:
draw = 0.0
num = 0
for trade in operating:
num = num + 1
draw = draw + trade["drawdown"]
turns.append([num, round(draw / 100, 2)])
for re in remove:
operating.remove(re)
operating.append(row)
# 最後一筆資料
draw = 0.0
num = 0
for trade in operating:
num = num + 1
draw = draw + trade["drawdown"]
turns.append([num, round(draw / 100, 2)])
# Result
# print(turns)
if len(turns) != 0:
# 最大回撤保證金(倉數) 回撤保證金倉數平均、 標準差
maxPosition = max([(a[0]) for a in turns])
drawdownHighestPosition = int(turns[np.argmax(turns, axis=0)[1]][0])
drawdownHighest = np.max(np.array(turns), axis=0)[1]
drawdownMean = statistics.mean((a[1]) for a in turns)
drawdownDev = statistics.pstdev((a[1]) for a in turns)
drawdownEstimate = drawdownMean + (3 * drawdownDev)
positionMean = statistics.mean((a[0]) for a in turns)
positionDev = statistics.pstdev((a[0]) for a in turns)
positionEstimate = str(round(positionMean + (2 * positionDev), 0)).split(".")[0]
# 收益/最大浮動虧損
#RD = statistics.NormalDist.from_samples(RevenueVSDrawback)
RDMean = statistics.mean(RevenueVSDrawback)
RDDev = statistics.pstdev(RevenueVSDrawback)
# RDmax = max(RevenueVSDrawback)
RDQuantiles = []
RevenueVSDrawback.sort()
for i in range(len(RevenueVSDrawback)):
for a in range(0, 11):
if i == int((len(RevenueVSDrawback) - 1) * a / 10):
RDQuantiles.append(round(RevenueVSDrawback[i], 2))
# Monte Carlo simulation
# monteCarloMaxDrawdown, monteCarloPosition = monteCarloSimulation(
# drawdownData, turns)
# Potential extreme maximum drawdown
potentialMaxDrawdown = potentialExtremeMaxDrawdown(drawdownData, turns)
# Position strategy
maxLoss = initialCapital * (maxLossPercent / 100)
strategyMaxDrawdown = max(drawdownHighest, drawdownEstimate)
safeTotalMargin = maxLoss / (strategyMaxDrawdown / drawdownHighestPosition)
safeMargin = round(safeTotalMargin / drawdownHighestPosition, 2)
# print(f"※常態分佈 = 保證金浮動虧損平均值 + 3 × 保證金浮動虧損標準差 (包含 99.73% 事件)")
# print(f"※潛在極端 = 將前 N 大單筆保證金浮動虧損組合進最大回合持倉內計算")
# print(f"※建議安全保證金 = (最大可接受虧損金額 ÷ 最大保證金浮動虧損 (％)) ÷ 最大保證金浮動虧損時持倉數")
# print(f"※建議安全總倉數 = 持倉數平均值 + 2 × 持倉數標準差 (包含 95% 事件)\n\n\n")
# print(f"※風報比 = 收益 ÷ 最大浮動虧損")
return {
"traderName": traderName,
"traderId": traderId,
"maxPosition": maxPosition, # 最大回合持倉數
"drawdownHighest": drawdownHighest, # 最大保證金浮動虧損 (倉)
"drawdownHighestPercent": round((drawdownHighest / drawdownHighestPosition) * 100, 2), # 最大保證金浮動虧損 (％)
"drawdownHighestPosition": drawdownHighestPosition, # 發生最大保證金浮動虧損時持倉數
"potentialMDD": round(potentialMaxDrawdown, 2), # 潛在極端最大保證金浮動虧損 (倉)
"potentialMDDPercent": round((potentialMaxDrawdown / maxPosition) * 100, 2), # 潛在極端最大保證金浮動虧損 (％)
"RDMean": round(RDMean, 2), # 風報比平均值
"RDDev": round(RDDev, 2), # 風報比標準差
"RDMid": RDQuantiles[5], # 風報比中位數
"initialCapital": initialCapital, # 初始資金（使用者設定）
"maxLossPercent": maxLossPercent, # 最大風險（使用者設定）
"safeMargin": safeMargin, # 建議安全保證金
"positionEstimate": positionEstimate, # 建議安全倉數上限
}
# My handmade function: FAILED
# def calcMaxMDD(traderId):
# load_dotenv()
# client = pymongo.MongoClient(os.getenv('MONGODB'))
# drawdownDB = client.TraderDrawdown.traderDrawdown
# traderData = drawdownDB.find({"traderId": traderId})
# traderData = (list(traderData)[0])
# # print(traderData)
# if len(traderData) == 0 :
# hasDrawdownData = False
# # print('Cannot find trader data in DB.')
# return False
# else:
# positionCount = 0
# positions = []
# suggestCapital = 0.00
# startTimestamp = int(time.mktime(time.strptime(f"2023-02-20 00:00:00", "%Y-%m-%d %H:%M:%S"))) + 3600*8
# endTimestamp = int(time.mktime(time.strptime(f"2023-03-10 23:59:59", "%Y-%m-%d %H:%M:%S"))) + 3600*8
# hasDrawdownData = True
# history = traderData["history"]
# for each in history:
# openTimestamp = int(time.mktime(time.strptime(each["openDate"], "%Y-%m-%d %H:%M:%S")))
# closeTimestamp = int(time.mktime(time.strptime(each["closeDate"], "%Y-%m-%d %H:%M:%S")))
# leverage = each["leverage"]
# drawdown = each["drawdown"]
# if startTimestamp <= openTimestamp and closeTimestamp <= endTimestamp:
# # print(f'{openTimestamp}, {closeTimestamp}')
# positions.append({
# "openTimestamp": openTimestamp,
# "closeTimestamp": closeTimestamp,
# "drawdown": drawdown,
# "overlapPos": []
# })
# overlap = np.zeros((len(positions), len(positions)), dtype="int_")
# for i in range(len(positions)):
# for j in range(len(positions)):
# if positions[i]["openTimestamp"] < positions[j]["closeTimestamp"] :
# overlap[i][j] = 1
# else :
# overlap[i][j] = 0
# for i in range(len(positions)):
# for j in range(len(positions)):
# if overlap[i][j] != overlap[j][i]:
# overlap[i][j] = 0
# overlap[j][i] = 0
# elif overlap[i][j] == 1 and overlap[j][i] == 1:
# positions[i]["overlapPos"].append(j)
# pass
# print(positions[64])
# # print(overlap)
# print(positions[256])
# MDD = 0
# for eachPos in positions :
# for eachOverlap in eachPos["overlapPos"]:
# MDD += positions[eachOverlap]["drawdown"]
# eachPos["overlapMDD"] = MDD
# MDD = 0
# return 0
if __name__ == "__main__":
response = copySimulate("4876423973")
# print(response["pnlInDays"])
# print(response["copyProfit"])
x = []
y = []
y.append(response["pnlInDays"][0])
for i in range(len(response["pnlInDays"])):
x.append(i + 1)
if i == 0:
pass
else:
y.append(response["pnlInDays"][i] + y[i - 1])
# print(analyzeTraderMDD("4876423973", 10000, 10, "2023-02"))